The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Bayesian analysis of elapsed times in continuous‐time Markov chains
Abstract
The authors consider Bayesian analysis for continuous‐time Markov chain models based on a conditional reference prior. For such models, inference of the elapsed time between chain observations depends heavily on the rate of decay of the prior as the elapsed time increases. Moreover, improper priors on the elapsed time may lead to improper posterior distributions. In addition, an infinitesimal rate matrix also characterizes this class of models. Experts often have good prior knowledge about the parameters of this matrix. The authors show that the use of a proper prior for the rate matrix parameters together with the conditional reference prior for the elapsed time yields a proper posterior distribution. The authors also demonstrate that, when compared to analyses based on priors previously proposed in the literature, a Bayesian analysis on the elapsed time based on the conditional reference prior possesses better frequentist properties. The type of prior thus represents a better default prior choice for estimation software.
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Metadata
- Authors
- Ferreira, M. A. R., & Suchard, M. A.
- Venue
- Canadian Journal of Statistics
- Publisher
- Wiley
- Published
- 2008-09
- Volume
- 36
- Issue
- 3
- Pages
- 355-368
- ISSN
- 0319-5724 (ISSN-L)
- 1708-945X
- OpenAlex
- W2002858652
- Cited by
- 309
- References
- 7
- FWCI
- 1.24
- Type
- journal article
- Language
- en
- Open access
- closed
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